Bibliographic record
Abstract
In our service-learning courses, students work with real people and record and reflect on these experiences, to learn appropriate professional behavior, how to think creatively, and how to respond to changing circumstances. Many of our students are strategic learners, characterized by alertness to assessment and intention to achieve the highest possible grades (Entwistle, Tait, & McCune, 2000). Their need to be correct often overrides the opportunity to explore ideas, troubleshoot, and problem solve. Their slavish allegiance to one correct answer prevents many from engaging in the messy processes of trial and error, formative feedback and assessment, reflection, and refinement (Dewey, 1938). They not only avoid the benefits of proximal learning, they also deny themselves the benefits of cognitive play that Vygotsky (1962) encourages. An end of term binge, their rush to get work done at the eleventh hour, can occur because many seem reluctant to take advantage of formative feedback opportunities (i.e. fine tuning) during the term. Accompanying this binge is the concomitant expectation of immediate feedback from the instructor, and the equally unrealistic expectation of their own spontaneous comprehension of the material without adequate assimilation time. This paper will describe our efforts to implement formative assessment in our classes. We present a number of formative assessment examples, discuss the pros and cons of teaching this way, and suggest some implementation strategies that enhance student motivation and timely engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".